Correction model based ANN modeling approach for the estimation of Radon concentrations in Ohio
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University of Toledo / OhioLINK
2012
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341604941 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-toledo13416049412021-08-03T06:08:19Z Correction model based ANN modeling approach for the estimation of Radon concentrations in Ohio Yerrabolu, Pavan Electrical Engineering Artificial neural networks Correction Model Indoor Air Quality Measures Interpolation Ohio Radon Zip code According to National Cancer Institute, radon is one of the major causes for lung cancer related deaths after smoking in the United States. In order to prevent deaths due to radon inhalation there is a need to determine the level of radon concentration in each locality, e.g., zip-codes. However, factors like inapproachability hinder the process of estimating radon concentration in some places. In such places the radon concentrations could be estimated using several interpolation techniques. In this thesis, a new approach that improves the accuracy of the neural model with the help of sensitivity based correction model for modeling and estimating radon concentrations in Ohio is proposed. The results are compared with commonly used techniques such as kriging, radial basis function (RBF), inverse distance weighting (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI) and the recently developed conventional ANN modeling approach. Further, model accuracies of all the above interpolation schemes are evaluated based on the ranked performance measures criteria with emphasis on the extreme-end (peak-end, low-end), and mid-range radon concentrations. The results demonstrate the effectiveness of the proposed approach in estimating the radon concentrations. The prediction accuracy of the proposed approach is found to be improved by 70-80% compared to the other techniques. 2012-09-27 English text University of Toledo / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341604941 http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341604941 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Electrical Engineering Artificial neural networks Correction Model Indoor Air Quality Measures Interpolation Ohio Radon Zip code |
| spellingShingle |
Electrical Engineering Artificial neural networks Correction Model Indoor Air Quality Measures Interpolation Ohio Radon Zip code Yerrabolu, Pavan Correction model based ANN modeling approach for the estimation of Radon concentrations in Ohio |
| author |
Yerrabolu, Pavan |
| author_facet |
Yerrabolu, Pavan |
| author_sort |
Yerrabolu, Pavan |
| title |
Correction model based ANN modeling approach for the estimation of Radon concentrations in Ohio |
| title_short |
Correction model based ANN modeling approach for the estimation of Radon concentrations in Ohio |
| title_full |
Correction model based ANN modeling approach for the estimation of Radon concentrations in Ohio |
| title_fullStr |
Correction model based ANN modeling approach for the estimation of Radon concentrations in Ohio |
| title_full_unstemmed |
Correction model based ANN modeling approach for the estimation of Radon concentrations in Ohio |
| title_sort |
correction model based ann modeling approach for the estimation of radon concentrations in ohio |
| publisher |
University of Toledo / OhioLINK |
| publishDate |
2012 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341604941 |
| work_keys_str_mv |
AT yerrabolupavan correctionmodelbasedannmodelingapproachfortheestimationofradonconcentrationsinohio |
| _version_ |
1719431518738186240 |